Modelling Mental Health Risk Factors in Environmental Research and Public Health
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".
Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 278
Special Issue Editor
2. School of Medicine, Deakin University, Geelong 3220, Australia
Interests: machine learning; deep learning; biostatistics; public health; mental health; trauma research
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mental health is a prevalent issue globally. Estimates from a systematic review and meta-analysis of mortality among people with mental disorders by Walker et al. in 2015 indicated that “mental disorders rank among the most substantial causes of death worldwide” [1]. With depression as one of the leading causes of disability and with suicide being the fourth leading cause of death amongst 15–29-year-old people (WHO), further research into the effective modelling of mental health risk factors in environmental research and public health is needed.
However, the modelling for this type of research is complex and the techniques are continually evolving. For example, risk factors have progressed from being static (e.g., sex, life stress incident) to the more modifiable types (e.g., diet, physical exercise). The advancement in complex statistical, machine learning, and deep learning modelling techniques and the growth of visualisation options (e.g., geospatial mapping, network analysis) has equipped researchers with greater tools to investigate mental health risk factors and effectively communicate the results. Often, mental health data contain a large, complex, and often heterogeneous array of potential risk factors. These factors can reside with the individual and/or surround the individual (e.g., family, community, country, institution etc). The nature and scale of mental health data has also developed in recent decades. For example, the use of linked health data has progressed to the use of time-based risk factor models. Papers with a high academic and analytical standard that provide new and novel models of mental risk factors specific to environmental research and public health are invited to contribute to this Special Issue.
Reference
[1] Walker, E.R.; McGee, R.E.; Druss, B.G. Mortality in Mental Disorders and Global Disease Burden Implications: A Systematic Review and Meta-analysis. JAMA Psychiatry 2015, 72, 334–341. https://doi.org/10.1001/jamapsychiatry.2014.2502.
Dr. Joanna Dipnall
Guest Editor
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Keywords
- mental health
- mental health disorders
- depression
- suicide
- mental health risk factors
- mental health risk models
- mental health machine learning
- mental health deep learning
- psychometric mental health model
- structural equation model
- predictive mental health risk factor model
- mental health environmental model
- mental health network analysis
- mental health linked data
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